AI Agents Are Changing the Internet

Speaker A: The information economy has arrived.

Speaker B: The world is teeming with innovation as

Speaker A: new business models reinvent every industry. Inside Analysis is your source of information and insight about how to make the

Speaker B: most of this exciting new era.

Speaker A: Learn more at InsideAnalysis.com InsideAnalysis.com and now here's your host, Eric Cavanaugh.

Speaker B: All right, ladies and gentlemen, hello and welcome back once again to the only coast to coast radio show all about the information economy here in the US of A. And folks, we're dialing in today talking to a guest from all the way on the other side of the planet. She is quite the expert in data and analytics and she is, uh, a key player at the SP Jain School of Global Management. She also writes books about AI and even uses some AI in the mix somewhere. Dr. C.J. meadows. Go to drcjmeadows.com to learn more about her and we're going to find out what she thinks about data and AI in this crazy world that we're living in. So, Dr. Meadows, thanks so much for your time today. Welcome to the show.

Speaker A: Thank you so much for having me. And call me cj.

Speaker B: CJ Sounds good. Okay, CJ Uh, we're in a very wild world right now in terms of technology. I mean, these technologies are so crazy and I don't think people really understand what the agentic world is going to be like. I saw a stat today, there's a lady may even know or have seen online, Sandy Carter. She said that there was a stat someone threw out just the other day that said, you know, just recently bots passed humans in terms of web traffic. And the prediction is that within X number of years, like a few years, bots will be 100 to a thousand times more ever present in the web than humans. So, so like a thousand times more bots than humans on the Internet. And in fact, Cloudflare even just released a new service, I think they call it Kite Surf or something like that, that is designed specifically for agentic traffic, which is pretty clever because humans need tabs and we need, you know, graphics and different things to work our way around, but agents don't. So they came up with a whole solution catering to the agents. Holy Christmas. What do you think about what's happening? Do you think that many, um, people in the world even really appreciate the magnitude of this inflection point?

Speaker A: Yeah, good question. And I hadn't even appreciated the magnitude of that. Um, you got to stop and think though. Agentic, what does that actually mean? It means not only going out and ordering products for you and Booking your airline tickets. It also means looking up information on other systems systems. Now uh, in one regard this is a welcome change for two reasons. Number one, people don't want to do stuff, but they want stuff done. So the governance and the responsible AI is super, super, super important. Number two, there's a difference between what people say and what they do. Their espoused values and their enacted values. And in marketing this is super important. You know, we market mainly to humans today, but the humans say things like, oh, I want eco friendly products, I want ah, sustainability along the supply chain with the, you know, from the farmers to the people who serve me, et cetera. And yet they don't have time to pay attention to all the information necessary to make decisions aligned uh, with their values. Now we can have agents do that. So there's a big move in marketing to not to market to people anymore, but to market to agents. And as a matter of fact people getting jobs. There's a huge shift in oh, actually you're not writing your CV and your LinkedIn profile for humans to read, you're writing it for AI to read, to scan. Um, and companies themselves, like a quarter of them have fully automated the hiring process and many of them are having not just AI analysis of your body language, your tone, your fidgetiness, but also having the AI interview you. So you've got to have your AI coach get you ready to interview for a human position with an AI and pretty soon they need to be on the org chart.

Speaker B: Yeah, that's it. You brought up a couple of really interesting points and I could just dive right down one of those wormholes when I look at hiring and uh, this is a personal theory that I have. I think the era of large employers with lots of employees and benefits and all these rules, I think it's fading and it might even crash and burn somewhat majestically and you're going to see a lot more people doing jobs, contracts, so called gig economy, things of that nature. Because the old system is so cumbersome and it's very difficult. And you know, you look around and you see this early phase of tech companies laying people off and um, claiming it's because of AI. AI's got something to do with it. But you know, as I've said with other people on the show in the past, you know, there was some over hiring done during COVID and they, they're fairly inefficient large organizations anyway, that's part of it. But then they're investing all this money in AI and you're asking yourself what, you know, what really is going on here. But I guess that deposit, the theory I'll uh, posit to you is that the traditional model of big corporations hiring lots of people and having these top down hierarchies for organizational structures, I think that's all in jeopardy right now. What do you think?

Speaker A: Well, first of all, a lot of the firing is called AI washing, right?

Speaker B: That's right, yeah.

Speaker A: So firing for business reasons and they're like oh no, we're, we' AI enabled. So it's right. You know. And some of that is done by the tech companies saying look how good our tech is. You can fire your people too and change your economics. Um, so pinch of salt, look for the warning signs that this is not actually about AI.

Speaker B: Right.

Speaker A: So if we look at like organizations and stuff I've been talking for decades about. Yes. And you can actually see that happening. Um, I've been writing and talking for decades about the network to organizations, new forms of working, blah blah, blah. And really all that is about permeable boundaries. Do you have to be inside an organization or can you be outside working with it, et cetera. What an organization is at its core is simply a mass of uh, society, stable relationships and ongoing work.

Speaker B: That's a great insight. Go ahead. That's good.

Speaker A: Whether you're legally part of the organization full time or not, um, there's consultants and contractors who make a career out of consulting for particular companies, but they're not counted as part of the organization.

Speaker B: Mhm.

Speaker A: And sometimes there's reasons for that. So we're looking at organizations with permeable boundaries and that is more comfortable now than ever before. But I'd say what we're looking at is the ability of AI to take over part of management so that we can have more fluid and flexible relationships and get some of uh, the absolute waste out of organizations and have people focus on content instead of selling to other people and managing each other.

Speaker B: Well, you bring up a number of really good points too. And I've had these thoughts myself that ah, AI can find its way in any number of places inside the organization. And why wouldn't senior leadership be one of those? And my theory is that before too long senior executives are going to have to work with an AI for the company and the AI is going to suggest things and if they go with what the AI suggests, that's one thing. If they go against it, they're going to have to explain why. And then if it doesn't work out, the AI is going to hold them accountable. I mean, at a certain point, it starts to get creepy and scary because we do have to think very closely about what these systems are. And as you know, I'm sure there are lots of kinds of AI. There are deterministic AI systems, there are probabilistic AI systems. These new ones, the Gen AI, that's all probabilistic, and it really is just. They are just predictive engines for patterns of words. That's all they're doing. They're not really reasoning. They're not. It's not intelligent per se, but it looks like it is because the word patterns are so clean and so well organized and seem to have so many good ideas in it, but they will just make stuff up. So you got to be careful about what kind of AI you have. But I think that you're right that AI can infuse itself in all kinds of different ways and will. And that'll probably be good for the company. But what do you think?

Speaker A: Well, you're absolutely right that mainly Gen AI is a prediction engine. So when we talk about hallucinating, it doesn't do anything else. That's all it's designed to do. And frankly, you know, you go talk to your philosopher friends and they'll probably tell you, you know, that's all we do too.

Speaker B: That's funny.

Speaker A: Is this reality or is this my conception of reality? And I'm like, oh, my God, I need coffee. So, um, yes, everybody, every time we have a new technology, be it mills for textiles or the computer itself, envision a dystopian future and all the fears come out and then nothing happened.

Speaker B: Right.

Speaker A: So, you know, will, we all have to work with the AI we actually do already. Number one, have a look at your phone. Yeah, Almost all of it is AI enabled in some form or another. Um, leadership and explainability of decisions. You have to explain what your systems do automatically as a company, and you have to explain the policies of how your humans behave and how you're holding them to those policies. Now, AI governance and responsible AI, we've got to lay that in as a foundation. Then with those guardrails, we can have the AI do stuff and the humans do too. Now, does every leader have to have an AI? Number one? Yeah. Because man plus machine beats man or machine every time. Leverage the power, have the synergy. We also need things like digital twins. I've got one. You may have one. And they stand for us with the understanding that that's a representative of the person, the way your attorney is a representative mhm. What are your thoughts on all these?

Speaker B: I'll tell you, uh, it's a wild and woolly time right now is what I would tell you. And it's the one thing I'd throw out there for sure for young people especially, but even middle aged people is do use the technologies, play around with these things to see what they can do, what they're able to do to. You have to be careful about where and how you implement them. But in terms of stochastic purposes, just discovery and education, it's incredible. For anything that is well documented and well published about, they're going to be very good because they've been trained on this massive corpus of text. But it's just words. As my buddy Mark Manson says, large language models do not model data, they model language. And language is not data. It can be. I mean the language that was used to train the models was essentially the data data to train those models. But data typically is things like transactions, amounts, what happened where, and for even unstructured data. But data is the, is the reflection of reality and the words are what we use to describe it. So what's happening is people are trying to use large language models to model data. And that is a big no, no, like don't do that. So don't throw the baby out with the bathwater. You want to do the old things you were doing, just do them more efficiently and use AI to, to infuse and optimize things. That, that's what I see.

Speaker A: Well, you know, in, in 2021, LLMs did not have the ability to ask good questions, but today they do. I'm, um, co authoring a book series on of all things, the Bible and God with an AI. And we've moved from prompts to dialogue, conversation. We build on each other's ideas. There is some semantic processing, not just predictive processing, but you have to understand more deeply how an AI, do they understand the world? Do they believe anything? No, they do not.

Speaker B: Right.

Speaker A: And they think differently from humans. But also if you're looking at education, you touched on that a minute ago. Ah. At SPG Global, we use a proprietary AI tutor for a whole host of things, but it talks with people. We put neural headsets on people to map how your thinking style is. Wow. And it adjusts to your thinking style and what you want to do with your learning afterwards. So there's a very different way to learn if you're headed towards marketing or if you're headed towards doing a startup or something else. You know, but the power of this stuff. And there was an MIT study. This is the last thing I'll, I'll say before I turn it over to you, but there was an MIT study showing with MRIs, that the use of AI sort of deadens the part of your brain that education used to awaken. But it didn't examine everything, and it compared with old development models, but it didn't compare with new. So I would say that your brain is going to develop and grow differently with the use of, uh, new tools than it did before. And you may find that this is so much better. And that by taking away some of what we have to think and some of what we have to do, that we're forced to get a grip on what makes us human and what makes me me, and what can make me a more extreme, unique and valuable version of me. And AI can help.

Speaker B: Yeah, you bring up a number of good points there. Uh, it's like calculators. Once calculators came out.

Speaker A: Yes.

Speaker B: People got less good at math because they didn't need to. Or your cell phone remembering phone numbers. You don't have to remember phone numbers anymore because they're on your cell phone. And so whatever, you don't have to remember. You don't. Then of course, you lose your cell phone and you don't know.

Speaker A: I'm sorry, I'm going to interrupt right now. But you see, you can either have a cell phone to remember numbers or do what my husband does. Get a wife.

Speaker B: That's pretty funny. Yeah. There are, there are certain things that, uh, partners are better at in relationships, and that's good stuff. Which actually brings us to another whole topic that I think we can pick up in, in some depth in the next segment here, but organizational hierarchies, organizational structures, things have changed so much now that the onus is on different people to do different things. And the, the example I'll give you here before we go to break is coders and development. For the last, let's say 40, 50 years, coders, the actual people hacking out code, have been the primary bottleneck in innovation, in the enterprise, at least in digital innovation, or digital transformation, as people refer to it. For 45, 50 years, coders were the bottleneck. Well, now, guess what? You can code with reckless abandon and create tons of code. Now you have to vet it, you have to be careful, as people will say, it's good for a web app or something like that, vibe coding, but you don't want to run, you know, Fortune 500 companies, ERP on that kind of code. You have to be very careful. But the fact of the matter is things have changed dramatically. And I would argue now the onus is on the business to figure out what business decisions they're going to make, what functionality we want next. That's where the next roadmap happens. And it's, it just is completely turned around. It was these guys over here and now it's us. It's like, oh no, we have to do a better job. But folks, don't touch up that. I'll be right back talking to Dr. C.J. meadows. Go to drcjmeadows.com for more information. And we'll be right back. You're listening to Inside Analysis.

Speaker A: Welcome back to Inside Analysis.

Speaker B: Here's your host, Eric Kavanaugh. All right folks, back here on Inside Analysis already a fascinating conversation with Dr. C.J. meadows. She is calling in from way across the pond over there in Singapore. She's with the SP Jain School of Global Management. You can find information about her@drcjmeadows.com and, uh, Dr. Meadows, C.J. what do you think about my, uh, my assessment? I think you agree based upon your, uh, your body language.

Speaker A: Well, actually mostly I'm laughing because I'm an old coder from the 80s.

Speaker B: Wow.

Speaker A: And you're right, it was so incredibly slow. Um, and we had to have loads and loads and loads of people do it to keep up with, uh, work. And you know what we as humans did what we built into every system mistakes.

Speaker B: Right.

Speaker A: And you know, we did not anticipate that our systems would be lasting 20 or more years. Um, although we did absolutely the best we could making sure things are right. But yeah, companies have been struggling for a long time with legacy systems and how to handle the problems that that makes not only for ongoing and changing business, but also for AI that, you know, the questions we're asking, AI, they go across the organization, they hit multiple systems. Different bits of data are called different things in the different systems. Um, so one, you can throw out your old legacy systems and replace them. That is hugely expensive. But places like DBS have actually done it and been reaping the benefits. Um, number two, Oracle companies like that say, okay, okay, okay, you've got to have a data lake or a data fabric. We've got to do all this work on the data and the systems beforehand, then put the layers on. Um, there's also digital twinning, not just of physical, ah, assets or even of people, but of your digital assets. So you can make a digital twin of your legacy system. And that layer can be a lot quicker and cheaper to put in than throwing out the core system that you have in place and then your AI can work off of the digital twin. Um, so percipient at Accenture does that. Another one that I spoke to somebody said no, you don't have to do all of that, just make an agentic layer. So there are a number of ways to handle these kind of problems. And yet the data itself, the quality of the data and the comparability of the data to use in your data analysis, that remains a tricky part. But yes, you've got to use AI in all its forms to deal with the situation and synergize with the tech folks to handle all these problems. And yes, it's going to be a lot faster.

Speaker B: Yes.

Speaker A: What we're seeing now is CIOs realize that the folks at the top of the organization, or even in the IT part of the organization, don't know how the hell the organization actually runs. It's the people at the bottom who do. So you've got to set the responsible guardrails and all that stuff. But the innovation needs to happen directly from the people at the bottom and, and they can finally do that without getting clogged up in the technical difficulty.

Speaker B: Mhm. That's bringing up a couple really good points here. And these are also things I've thought about. If you have an organization that really does embrace this gen AI wave and uh, there are so many things, let's just pick up on sales. For example, Gong was one of the first to come along and add this really interesting perspective where they are using an algorithm to sense tone of voice. And when you're on a sales call, they're actually monitoring that and then giving you feedback saying hey, you're losing this guy or hey, she really likes this. Make a recommend this. People who can use that kind of tool well can really excel. It's very valuable. But now if all, uh, if you have like let's say a thousand salespeople and let's say you've got you know, 100 sales directors or with 10 people reporting each to them and then you got a couple people up top, the person up top can get a summarized version of what all the directors get and they can all get a summarized version of what all the salespeople are doing and all of a sudden you have this amazing visibility up, down, left, right, all throughout the organization. That is a huge step change compared to how things used to be. Because now you can be watching in real time you could be defining best practices by the day. I mean there are so many things that you can do if you do it properly. What do you about that?

Speaker A: Absolutely. And by the way, not only does is, you know, can the AI give you wonderful valuable feedback, but a key core practice of salespeople and call centers is a low tech thing. Just put a mirror in front of your people so they can see if they're smiling.

Speaker B: Yeah, right.

Speaker A: Okay. So don't forget stuff like that.

Speaker B: Right. The basics.

Speaker A: A friend of mine founded Streams AI and, and it's amazing. The core concept was in a sales organization having a manager of 10 people. If that's a really fabulous manager, who encourages, facilitates, coaches, all this great stuff that becomes a top sales team. Well yeah, there's a bottleneck. That person can only be in charge of 10, maybe 30 people. What if that was an AI in charge of 50,000 people?

Speaker B: Mhm.

Speaker A: And then you start getting into situations where you develop a super manager, a super coach, a super facilitator to help your entire workforce. But like you just said, it learns from everybody and brings up the best practices real time. Do I think that's dystopian? No, not as long as the core and the foundation of the human culture as well as the tech is that it's supportive, that it's helping the humans become better at what they do and grow and flourish. What you get as a problem is organizations that don't have a positive culture, which is the humans fault.

Speaker B: Right.

Speaker A: And they put in the tech to track and over control. Then what do you think the humans are going to do? They know better than anyone else how to screw up the tech and the organization and get their own back. So when you're doing this, lay in that foundation of ethics and responsibility and some feedback guardrails from the people themselves and you know, make sure you're a human designed organization. As if people mattered.

Speaker B: Mhm. Yeah. There's a company I just interviewed the other day and I really love their approach. It's called Haifa Hypha Co. They do asset intelligence, which is some very interesting stuff. It's, it's, I would call it the bleeding edge of business intelligence. What they do is they built their own, he uh, calls it a topology. It's like Palantir's ontology. And he's tracked the end to end life cycle of asset management and lending for assets like commercial real estate skyscrapers. If you're going to loan a company $100 million to buy a skyscraper, you're going to want to Do a whole heck of a lot of research on everything involved on the neighborhood, the building, the asset, everything about it, you want to know. And he's really dug deep into that. But his whole approach is what he calls a human first approach, which is brilliant. And I think a lot of the pushback, and this is an interesting subject to dive into. There's a lot of pushback among Gen Z about AI in general. I don't know if you've seen it here in the States. At some of these commencement ceremonies, when the person brings up AI, the crowd boos. Right. There's a girl that, uh, I talk to. I get milkshakes from my wife all the time. So I know all the girls at the ice cream shop. And the one had a degree in environmental science and I asked her, oh, have you used chatgpt yet? Uh, and she was terrified. She's like, no, I'll never use that. I'm like, why not? She said, it's ruining the environment. And I thought to myself, boy, this needs to be addressed. Because, you know, I could complain all day long about the media. I've got a theory that the narrative is always wrong. Because it's a narrative, it's not reality, it's a story. And the media tends to exaggerate things and pick up on conflict and really cause trouble, if you get right down to brass tacks. But when I look at this, this young kid, I'm thinking, this kid needs to get over this fear of using these technologies because she's not going to do well in the real world. She's a very smart kid. But what do you think about all that?

Speaker A: Well, first of all, she needs to know that, yes, as Luddites, we could take our hammers and go and beat the crap out of all of the machines making new textiles. Okay. The Luddites happened a long, long, long time ago. Basically, they lost. Um, you need to use technology to find out how to handle some of these environmental problems. And I would posit that that's the only way we're going to actually address them. Yes, we have to address the human side of them. But the technology itself can come up with ways. Uh, for example, city traffic. All right. If you use AI to optimize the traffic flow within an entire city, you can lower the carbon footprint hugely.

Speaker B: Mhm.

Speaker A: All right. And there's all sorts of other applications that, that you know, that you can apply the AI to for sustainability. So first of all, use it for good.

Speaker B: Mhm.

Speaker A: Don't just run away. Because if you don't use it for good. Other people are just going to use it, period.

Speaker B: Right.

Speaker A: All right. So it's your job to put the for good in there. Now also, I've got four kids and there's the job problem right now because organizations are saying AI washing. Oh, we don't need young people, we just need the AI. And organizations are finding very, very frankly that that's not true. But what we are moving towards is more of an obelisk type organization from the pyramid.

Speaker B: Mhm.

Speaker A: Then what we're finding is that young people need to be entering at a higher level than they used to. Okay. Instead of walking into the consulting company and doing all the photocopying of the IT manuals or walking into the legal office and you're going to go through the huge legal library looking up cases for the, for the, you know, the things that the lawyers are doing, etc. Some of the tasks are taken over by technologies, but it's not the jobs. Jobs need to be redesigned and companies are actually doing that. And it's up to you as young people to find ways to get the experience. Do the simulations.

Speaker B: Mhm.

Speaker A: Go into the newer, more updated educational programs to up your skills beyond that old entry level so that you become a value creating resource and can join organizations. Or more importantly, make your own damn organization. And go back to what we talked about earlier with the network organizations and gig work.

Speaker B: Mhm.

Speaker A: Which is bigger than ever before.

Speaker B: Yeah, I agree with you 100%. I think that's exactly correct. I think that you have to respect. Well, I'll put it to you this way. We're planning an event here in Pittsburgh called the Agentic Roadshow. It's going to be this fall. And uh, we're going to do just a half day. And then the folks at the University of Pittsburgh who are going to host us, the lady made a really interesting comment. She said, you know, there is a lot of this backlash against AI among the young people. Maybe we should address that. You know, especially for graduate students and seniors, basically people who are near the edge and entering into their careers. And I thought to myself, you know, that's a really, really good point and I want to, to speak to that. So I'm probably going to do a morning keynote on the backlash, addressing the backlash. And what I plan to do is uh, Victor Hugo, I'm glad I remembered his name. Before I give the quote, he had this quote that will just. It just blows my mind every time I think about it, every time I say it. And we'll pick this up after the break in about another minute and a half here. But he has this great line where he said, no army in the world can fight an idea whose time has come. You know? And I remember the first time I read that, I was like, holy Christmas. That is just wild, because what are you gonna do now? I will say that you, uh, know, we up here in Pennsylvania, we're near Amish country, and the Amish are absolutely amazing. They can build whole houses in, like, a day. They moved this barn all the way across the state and put it up, like, near our house just around the corner. They do amazing things. They've gone into hurricane disaster zones, and they offer to rebuild their houses for free. And they are classic Luddites. So it's like, you know, wherever you focus your attention, you can get very good. And I have a lot of respect for craftsmanship. But, you know, if you don't want to be Amish or live that lifestyle and you want to live in the rest of the world, if you will, you better be looking at this AI stuff and understanding what it does, because in many ways. And we'll pick this up in just a second. But I view this AI wave is kind of like the new cloud. Because if you think about how cloud computing changed, computing in general, in business, it was a big deal. It took a while to take off. It took longer than I expected. It kind of took Microsoft and Chata Nadella to say, hey, we're going cloud, folks, because that's what brought it all up to the cloud. Now, you know, things are changing again, and there's not, uh. It's not true that cloud is better for everything. It's not. There are some use cases where on prem is still just fine. But, folks, don't touch that job. We'll be right back. You're listening to Inside Analysis.

Speaker A: Welcome back to Inside Analysis.

Speaker B: Here's your host, Eric Kavanaugh. All right, folks, back here on Inside Analysis, a fantastic conversation with Dr. C.J. meadows. Hop online to drcjmeadows.com to check out her work and her books. She's also with the SP Jain School of, uh, Global Management, which sounds, uh, very interesting as well. But CJ, again, look at what's happening. This is a massive shift. I'll just give you an example. I went to this event in Italy, uh, a few weeks ago that was awesome. I love Italy. Bologna. It was the We Make Future event. And after it, I had this great idea for a new show for an Italian TV show that I call La Prosima. Volta, uh, which means the next try, literally in Italian. And the idea is that there's always a failure in some IT evolution. If you're trying to build a system or do something with technology, there's always some failure along the way, which leads to the big success because you learn some lesson. You're like, oh, that's great. Well, I had this idea and I wrote down the tenets of the show, and then I used ChatGPT to kind of write a three page document about it. I checked it out, it was pretty good. And then I said, now give me a one sheet to give me a poster to sell this thing. And it, uh, worked for a little while and it cranked out this absolutely gorgeous, perfectly laid out, designed graphic that showed all the different segments and had, you know, it had Vespas in it and espresso and everything is. It's Italian. And I'm thinking, man, in the old days, meaning three years ago, it would have taken a whole marketing team, like two to three weeks to have meetings and go over client engagement and talk about ideas and do a. Do storyboards and all this stuff. And I did it in like an hour because I had a good idea and I knew how to articulate it to the machine. That is a massive change. Now, that doesn't mean you should fire your designers. What it means is you got to be working on more stuff, getting more product out the door, new service offerings, new products, whatever. That's what I take out of it. But what do you think?

Speaker A: Well, I'd like to. I'd like to tap on some of what you mentioned before the break.

Speaker B: Yeah.

Speaker A: And then also respond to this. Well, no, let me do this one first, because it actually is in my head. Uh, things wash out of my head. My digital twin, she remembers everything.

Speaker B: That's funny. That's funny.

Speaker A: Um, so first of all, you just said why that worked. You had a good idea and you communicated it well. And frankly, you didn't have to sell it to a team of humans who didn't understand your idea and won't understand it until you produce a prototype. I mean, this is the power of combining data analysts and design thinkers. M. One finds problems with data and, and quantitative stuff. The other one finds out why is that a problem with qualitative data and investigation. Um, so, yes, I agree that the good ideas can come to life now, and you can prototype like that. Now, if we go back to cloud computing and the Amish, what a great combination that is, Right? All right, so cloud computing. Yes, there is A role for some local computing as well. And the Fido 2 architecture for payment systems is the point. You know, a great case for that. You know, the, the way it was designed was also a design thinking story. And the engineers said, well, how might we build a secure technology verifying humans the way we do, the way we learn to trust each other.

Speaker B: Interesting.

Speaker A: And they came up with, well, we trust each other based on intimacy and relationship. Everybody has a relationship with your phone. So your pin, your face, your fingerprint, that all stays local. And the AI in your phone then talks to your bank without revealing any passwords and says, yeah, I verified my human, now let's verify each other. So the combination. And again, we get back to combinations. The way humans think, the way AI thinks, put them together is a very valuable combination. Now, the Luddites and the Amish. The Amish are not Luddites. Uh, and the difference is this, Luddites were fighting against something. The Amish fight for something nice. They fight for preserving the humanity God in the midst of their lives and relationships.

Speaker B: Mhm.

Speaker A: And keeping those core capabilities is why they can do incredible things like go build a barn in a day. Entire community goes to the hurricane zone to build houses, and up they go. Um, so everybody, including the young lady at the shake shop, everybody has to ask, what am I fighting for? Or am I just fighting against something? And what exactly is it that I'm fighting for? And am, um, I doing that in a way that fosters flourishing?

Speaker B: Mhm.

Speaker A: Please, everyone redirect to that.

Speaker B: Yeah, I, I agree. I think that's a fantastic mantra and a great perspective to have. And you know, let's face it, there are honorable causes and dishonorable causes. I want to write about this. I've wanted to for years and years and years. And if you pursue an honorable cause, you will be rewarded with the feeling you have inside of you. You'll just feel good, uh, because you did a good thing right now, there are reasons for that. I'm like you. I believe in that spiritual angle for why that's the case. But the fact is, if you're a good person, you do good things, you will feel good. You won't feel guilty, you won't feel terribly anxious. You know, you'll be able to deal with all the sort of, the disruptions of life. Right.

Speaker A: You'll feel clean.

Speaker B: Yeah, yeah, that's. That's a good way to put it. You'll feel your conscience is clean. There's nothing better than a clean conscience. Right? That's what I'M talking about and like, uh, we'll kind of close out on this. One thing that really does annoy me. And I had uh, a lady on the show who also made a very good point about this. I think Dr. Lisa Palmer is her name. We're talking about this, uh, AI washing and companies saying, oh, you know, we're going to lay people off because we're more efficient or whatever. And she said if you tell your employees that you want them to use AI because you're going to replace them, they're going to be wildly unmotivated to help you on that. Cause it's just not smart. It's a very, it's a very negative perspective. And I don't even like the term headcount, for example. That bothers me. I don't like the term human resources hr. I just don't like that because we're people, we're all people and we're all equal. So if you focus on the human first, I think we're going to be fine. But what do you think, Dr. Meadows?

Speaker A: Absolutely, I do agree. And you know, the message that needs to go out to your people and be backed up by action is no, we don't intend to fire everybody. We intend to humanize what we all do.

Speaker B: Mhm.

Speaker A: And focus on what do humans do best. And you know, if that means that we free up resources, that means that we all need to learn and we need to learn about those we serve and create new products, new services, new experiences, new what, whatever to grow our organization. And what organization doesn't want to grow? And if you look at most of the world, organizations tend to focus on the wealthy consumer. But disruption happens at the bottom. So how might we take what we offer and offer it to everybody else in an affordable, personalized, effective way?

Speaker B: You hm.

Speaker A: Can grow from a few hundred million consumers to four and a half billion.

Speaker B: Mhm.

Speaker A: Pretty damn quick.

Speaker B: Yeah, that's a good point that you make. And I think it kind of speaks to the organizational change and the change management that organizations have to go through right now. And it's all about finding new important things to do, which could be research, it could be education, it could be outreach, it could be just talking to people. But things do need to change. I mean that's the bottom line is that things have to change and fast. Final thoughts from you. Go ahead.

Speaker A: You know, we all get used to all of the problems and injustices that we surround us every day. But if we become more efficient with AI, free up some human resources and use the power of AI. We can finally start to address what's wrong that's all around us.

Speaker B: Mhm.

Speaker A: And make things better. Why would you not do that?

Speaker B: Yeah, right. Well, you know, again, people are concerned. People get lost, uh, in their day to day. You know, you get, you get into certain patterns and old habits die hard. So uh, I think that the key is to just slightly pivot. You don't have to change everything. Kind of like you're saying about enterprise systems, rip and replace is a very difficult thing to pull off. But you do want to pivot and know where you're going and go where, where it feels right. That's one of my big things too. If it feels right, it probably is right. There's probably some reason why you want to go do X, Y or Z. But just be careful as you're going along the way and make sure that, you know, you can still pay your bills and things of that nature.

Speaker A: And can I touch on that for just a moment? Um, yeah. We all need to pay bills. I do too. But you don't do that by playing the old game.

Speaker B: Right.

Speaker A: You do that whether it's the old game or the new game, by finding the value and creating it for others and yourself.

Speaker B: I love it. That's a great closing line. Well, look up drcjmeadows.com Fantastic interview. Thanks for all the work you're doing. We'll talk to you next time. Folks. You've been listening to Inside Analysis.